WO2011016198A1 - Method for acquiring hair characteristic data and device for acquiring same - Google Patents

Abstract

Disclosed is a method for acquiring hair characteristic data which comprises an image-acquiring step and a data-acquiring step. In the image-acquiring step, a cross-sectional image of human hair (50), in which multiple kinds of fibrous tissues [ortho cells (52a) and para cells (52b)] constituting cortex cells (52) contained in the hair (50) are visualized in such a manner as to be distinguishable from each other, is acquired. In the data-acquiring step, numerical data indicating the distribution state of the visualized multiple kinds of fibrous tissues [ortho cells (52a) and para cells (52b)] is acquired from the cross-sectional image.

Description

Method and apparatus for acquiring hair characteristic data

The present invention relates to a method and apparatus for acquiring hair characteristic data.

Generally, in order to objectively describe the degree of hair combing, curl radius, curl curvature, etc. are used as indices (Non-Patent Document 1). In addition, in order to objectively describe the physical properties of the hair related to the feel such as the firmness, firmness, and softness of the hair, the thickness, tensile elasticity, bending stress, etc. of the hair are used as indices (Non-patent Document 2). ).

The curl radius and curl curvature are generally calculated by actually measuring the overall curved shape of a single hair. In addition, as a method for evaluating comb hair, a method of analyzing the penetration rate of organic substances and inorganic salts into hair is known (Patent Document 1). In addition, paying attention to the bundle of fibrous tissues constituting cortex cells, the ratio of absorbance of amide I (C═O bond) and amide II (NH bond) contained in this bundle is determined from a cross-sectional image of hair. There has also been proposed a method for evaluating the degree of comb hair (Patent Document 2).

Human hair (head hair) consists of scaly (layered) cuticle cells that cover the hair surface, fibrous cortex cells that occupy most of the interior of the hair, and a medulla that forms a porous medulla in the center of the hair. It consists of cells. Among these, cortex cells existing in the hair are said to have at least two types of cortex cells, cells similar to wool paracortex cells and cells similar to wool orthocortex cells. (Non-patent Document 3).

Non-Patent Document 3 describes the relationship between the abundance ratio of these two types of cortex cells and the shape of the hair. Specifically, it is roughly divided into Asian human hair (Mongoloid), Caucasian hair (Caucasian), and Ethiopian human hair (African). It is described that there is a certain tendency between them.
Non-patent document 4 will be described later.

Japanese Patent Laid-Open No. 9-178738 JP 2006-170915 A

R. De la Mettrie, et al., Human Biology, Vol.79 No.3, pp.265-281, 2007 C.R. Robbins, "Chemical and Physical Behavior of Human Hair" 4th Ed., Springer-Verlag New York, Inc., pp.386-473, 2002 "Morphology and histochemistry of human hair" in "Formation and Structure of Human Hair", by J. A. Swift, P. Jolles, H. Zahn, and H. Hocker, Eds., Birkhauser Verlag, Basel, pp.149- 175, 1997 W. G. Bryson, et al., Journal of Structural Biology, Vol.166, pp.46-58, 2009

In the evaluation methods described in Patent Documents 1 and 2, it is difficult to accurately quantify hair properties because cortex cells are not evaluated by distinguishing them from a plurality of types of cells.
Non-Patent Document 3 describes the hair characteristics, although it is shown that the abundance ratio of plural types of cortex cells constituting human hair contributes to the hair characteristics. Quantitative considerations have not yet been made.

The present invention has been made in view of the above problems, and provides a method and an apparatus for easily obtaining quantitative information for describing hair characteristics.

The method for acquiring hair property data of the present invention includes an image acquisition step of acquiring a cross-sectional image of the hair in which a plurality of types of fibrous tissues constituting cortex cells contained in human hair are visualized so as to be distinguishable from each other. A data acquisition step of acquiring numerical information indicating the distribution state of the visualized plural types of fibrous tissues from the cross-sectional image.

Further, the hair characteristic data acquisition device of the present invention is an image acquisition means for acquiring a cross-sectional image of the hair in which a plurality of types of fibrous tissues constituting cortex cells contained in human hair are visualized so as to be distinguishable from each other. And data acquisition means for acquiring numerical information indicating the visualized distribution states of the plurality of types of fibrous tissues from the cross-sectional image.

In the above invention, the cross-sectional image of hair refers to an image obtained by capturing part or all of a cross section (transverse cross section) in a direction intersecting the hair axis. Moreover, as long as the cross-sectional image of hair here is an image in which the distribution state of the tissue in the radial direction of the fibrous tissue constituting the cortex cell of the target hair can be grasped, it relates to the physical cut surface of the hair. It may be an image or a transmission image.
The state in which a plurality of types of fibrous tissues are visualized so as to be distinguishable from each other in a cross-sectional image of hair refers to a state in which the plurality of types of fibrous tissues are identified from each other visually or by image processing means.

The various components (means) of the present invention do not have to be individually independent, but a plurality of components are formed as a single member, and a single component is formed of a plurality of members. It is possible that a certain component is a part of another component, a part of a certain component overlaps a part of another component, and the like.

According to the hair characteristic data acquisition technology according to the present invention, since the distribution state of the fibrous tissue constituting the cortex cell is acquired as numerical information, quantitative evaluation of hair characteristics, an appropriate hair treatment method, Objective selection of hair care agents is possible.

The above-described object and other objects, features, and advantages will be further clarified by a preferred embodiment described below and the following drawings attached thereto.

It is a block diagram which shows an example of the data acquisition apparatus concerning 1st embodiment of this invention. It is a cross-sectional schematic diagram of human hair. 2 is an entire image of a hair cross section in Example 1. FIG. FIG. 4 is an enlarged view of FIG. 3 and shows a cuticle cell. FIG. 4 is an enlarged view of FIG. 3 and shows cells classified as para cells (detailed later) among cortex cells. FIG. 4 is an enlarged view of FIG. 3 and shows cells classified as ortho cells (described later in detail) among cortex cells. FIG. 4 is an enlarged view of FIG. 3 and shows a medulla cell. It is a visualization image of Example 1. FIG. It is the whole image of the hair section in Example 2. It is a visualization image of Example 2. FIG. It is a whole image of the hair section in Example 3. It is an image of the R value of FIG. It is an image of G value of FIG. It is an image of B value of FIG. It is a visualization image of Example 3. It is a whole image of the hair section in Example 4. It is an image of the R value of FIG. It is an image of G value of FIG. It is an image of B value of FIG. It is a visualization image of Example 4. FIG. (A) is a scatter diagram and calibration curve data showing the relationship between the curl radius and the center-of-gravity distance for the reference hair, and (b) is the relationship between the bending elastic modulus for the reference hair and the abundance ratio of para cells (detailed later). It is a scatter diagram and calibration curve data showing.

Hereinafter, embodiments of the present invention will be described with reference to the drawings. In all the drawings, the same reference numerals are given to the same components, and the description will be omitted as appropriate.

<First embodiment>
FIG. 1 is a block diagram illustrating an example of a hair characteristic data acquisition device (data acquisition device 100) according to the present embodiment.

First, an outline of the data acquisition apparatus 100 of this embodiment will be described.
The data acquisition device 100 includes an image acquisition unit 10 that acquires a cross-sectional image of hair, in which a plurality of types of fibrous tissues constituting cortex cells of human hair are visualized in a distinguishable manner, and a plurality of types of visualization A data acquisition unit 20 that acquires numerical information indicating the distribution state of the fibrous tissue from the cross-sectional image.

A data acquisition apparatus 100 illustrated in FIG. 1 includes a digital camera 11 as an image acquisition unit 10 and a personal computer main body 21 as a data acquisition unit 20 connected by a communication line 30.
The image acquisition unit 10 acquires a cross-sectional image of human hair (not shown in FIG. 1) and transmits it to the data acquisition unit 20. Various means can be used as the image acquisition unit 10, and the digital camera 11 is an example.
As the image acquisition unit 10, an image scanner 12 may be used instead of the digital camera 11. That is, a cross-sectional photograph of hair may be taken, converted into image information by the image scanner 12, and transmitted to the data acquisition unit 20.
Moreover, the cross-sectional image of hair may transmit the cross-sectional image stored in the web server (not shown) to the data acquisition part 20 via the internet 13 and the communication line 30. FIG. In such a case, the web server and the Internet 13 function as the image acquisition unit 10.

Specifically, the data acquisition unit 20 of the present embodiment is a personal computer main body 21 that has a predetermined calculation function and functions as a calculation unit and a storage unit. The data acquisition unit 20 is accompanied by a keyboard 22 as an information input unit and a display 40 as an information output unit.
The information output unit outputs the distribution state of the fibrous tissue constituting the cortex cell and the evaluation result of the hair characteristics.
As the information output unit, in addition to the display 40, a printer 41 connected to the data acquisition unit 20 via the communication line 30 or the Internet 13 may be used.

Next, a method for acquiring hair characteristic data according to the present embodiment (hereinafter sometimes referred to as the present method) will be described in detail.
The method includes an image acquisition process and a data acquisition process.
In the image acquisition step, a cross-sectional image of the hair is obtained in which a plurality of types of fibrous tissues constituting cortex cells contained in human hair are visualized so as to be distinguishable from each other.
In the data acquisition step, numerical information indicating the distribution state of the plurality of types of visualized fibrous tissues is acquired from the cross-sectional image.

Hereinafter, each process will be described in detail.

<Image acquisition process>
In this step, a cross-sectional image of the hair of an arbitrary subject is acquired, and information indicating the morphology or structure of the cells inside the hair, physical properties, protein composition, chemical composition, and the like is acquired as image information.
More specifically, a step of visualizing a plurality of types of fibrous tissues constituting cortex cells contained in human hair so that they can be distinguished from each other (visualization step), and a step of acquiring a cross-sectional image of the hair (imaging step) Including.

A specific example of the image acquisition process will be described later. The execution timing of the visualization process and the imaging process is optional, and these processes may be performed simultaneously.
Specifically, a visualization process may be performed by staining a cross section of the hair, and the cross-sectional image of the hair may be acquired by the image acquisition unit 10 in a state where the fibrous tissues are visualized in advance so as to be distinguishable from each other. In this case, the imaging process is performed by the image acquisition unit 10 after the visualization process. The staining method and gene observation method described later correspond to this.
Moreover, you may perform simultaneously the visualization process of the cross section of hair, and the imaging | photography process of a cross-sectional image. The spectrum measurement method and X-ray scattering method described later correspond to this.
Moreover, about the hair in the state which cannot distinguish a fibrous structure visually, the cross-sectional image is image | photographed with the image acquisition part 10, and after that, a fibrous structure may be visualized by image processing so that identification is possible. In this case, the imaging process is performed by the image acquisition unit 10, and the visualization process is performed by the data acquisition unit 20. The TEM observation method and microprobe observation method described later correspond to this.

Here, the structure of human hair will be described. FIG. 2 is a schematic cross-sectional view showing a part of human hair 50.
As shown in FIG. 2, the hair 50 has a scaly (layered) cuticle cell 51 covering the surface thereof, a fibrous cortex (furty) cell 52 occupying most of the inside of the hair 50, and a hair center portion. And medulla cells 54 constituting the medulla (hair medulla) 53 present in the.
Japanese hair often has a medulla 53 porous in a sponge-like manner. Wool mainly consists of cuticle cells 51 and cortex cells 52, and in many cases is different from human hair in that it does not contain medulla 53.

Cortex cells 52 constitute the main part of human hair and contain cells and intercellular binding substances. The cortex cells 52 include atypical cortex cells in addition to ortho cells 52a and para cells 52b described later. The ortho cells 52a and the para cells 52b form a fibrous tissue around the medulla 53, respectively.

The cortex cell 52 is composed of bundles of fiber units called macrofibrils 55 having a diameter of about 0.1 to 0.6 μm.
The macrofibril 55 is constituted by a bundle of intermediate filaments (IF) having a smaller diameter (about 7 nm in diameter) gathered in a bundle.

In the present invention, among the cortex cells 52 of the human hair 50, a cell in which a plurality of macrofibrils 55 in the cortex cells are fused to form a relatively large micron-order domain is referred to as a para cell 52b. . Inside the macrofibril 55 forming the para cell 52b, a large number of intermediate filaments (IF) are oriented substantially parallel to the hair axis direction.
On the other hand, among the cortex cells 52, a cell in which a plurality of macrofibrils 55 having a submicron order size are gathered together while maintaining their respective forms is referred to as an ortho cell 52a. In the macrofibril 55 forming the ortho cell 52a, the IF is inclined in a spiral shape.
Therefore, based on the size of the macrofibril 55 and the orientation of IF, the ortho cells 52a and the para cells 52b can be visualized in a distinguishable manner.
Further, since the para cells 52b are oriented substantially linearly along the hair axis, the tensile elastic modulus is higher than that of the ortho cells 52a.
In addition, the para cell 52b of the human hair 50 has a structure similar to the wool paracortex cell or the mesocortex cell from the viewpoint of the macrofibril form and the IF sequence structure, and the human ortho cell 52a The structure is similar to orthocortex cells. However, the components and physical properties of human hair 50 ortho cells 52a and wool orthocortex cells, and human hair 50 para cells 52b and wool paracortex cells or mesocortex cells are different from each other. ing. Further, as described above, the occupancy ratio of medulla is greatly different between human hair and wool. For this reason, it is difficult to estimate the relationship between the composition of cortex cells in human hair and the hair characteristics from the relationship between the composition of cortex cells in wool and the hair characteristics.

Hereinafter, a plurality of specific examples of the image acquisition process will be described in detail.

(Image acquisition method 1. Staining method)
In the first example of the method performed in the image acquisition process, the cross section of the hair 50 can be dyed with one type or two or more types of dyes to identify a plurality of types of fibrous tissues (ortho cells 52a, para cells 52b). It is a method to visualize.
The cuticle cell 51, the two types of cortex cells (ortho cell 52a, para cell 52b), and the medulla cell 54 present in the human hair 50 are different from each other in their protein composition and form, and thus are stained with various dyes. Sex is different. As a result, a cross-sectional image reflecting the distribution state of each cell can be acquired by using an appropriate dye.

The dye used in this method is not particularly limited as long as it dyes only one of the ortho cells 52a and the para cells 52b. Then, by using a dye that substantially colors only the ortho cells 52a and a dye that colors only the para cells 52b, that is, two or more kinds of dyes in combination, both can be clearly distinguished from each other. . However, in consideration of the effect of hair color, it is preferable to use a fluorescent dye having fluorescence in a wavelength region different from the fluorescence derived from the hair constituents. As a specific example, the orthocell 52a can be stained with a specific color by sulferodamine having orange fluorescence, and the paracell 52b can be stained with fluorescein having yellow-green fluorescence or an alkali metal salt thereof. .

(Image acquisition method 2. TEM observation method)
A second example of the method performed in the image acquisition process is to observe a plurality of types of fibrous tissues (ortho cells 52a and para cells 52b) by observing the hair 50 with a transmission electron microscope (TEM). This is a method for obtaining a cross-sectional image reflecting the distribution state of the cells constituting the cell.
Since the cuticle cell 51, the two types of cortex cells (ortho cell 52a, para cell 52b), and medulla cell 54 have different protein compositions and forms, they are stained with an electron stain used in a transmission electron microscope. Sex is different. As a result, a cross-sectional image reflecting the distribution state of each cell can be acquired based on the difference in the morphology of each cell observed with a transmission electron microscope.

(Image acquisition method 3. Spectrum measurement method)
A third example of the method performed in the image acquisition step is to measure an infrared absorption spectrum or a Raman spectrum related to a cross section of the hair 50, and use a plurality of types of fibrous tissues (ortho cells 52a and para cells 52b) as cross section images. This is a method of visualizing them so that they can be distinguished from each other.
Since the cuticle cell 51, two types of cortex cells (ortho cell 52a, para cell 52b), and medulla cell 54 have different protein compositions and forms, they are Fourier transform infrared spectrophotometers (FT-IR). The infrared absorption characteristics measured with are different. Therefore, if an appropriate infrared signal is selected, a cross-sectional image reflecting the distribution state of each cell can be acquired based on the signal intensity or the ratio of a plurality of signal intensities.
Further, the cuticle cell 51, the two types of cortex cells (ortho cell 52a, para cell 52b), and medulla cell 54 have different Raman spectra obtained by irradiating with a polarized excitation laser. Therefore, if an appropriate Raman spectrum band is selected, a cross-sectional image reflecting the distribution state of each cell can be acquired based on the signal intensity or the ratio of a plurality of signal intensity.

(Image acquisition method 4. Microprobe observation method)
A fourth example of the method performed in the image acquisition step is a cross-section reflecting the distribution state of a plurality of types of fibrous tissues (ortho cells 52a and para cells 52b) by observing the cross-section of the hair 50 with a microprobe microscope. This is a method for acquiring an image. Here, the microprobe microscope is a general term for microscopes that obtain information on a fine surface area by scanning a probe (probe) having a sharp tip on a cross section of hair as a measurement sample. As a microprobe microscope used in this method, an atomic force microscope (AFM) can be exemplified.
The cuticle cell 51, two types of cortex cells (ortho cell 52a, para cell 52b), and medulla cell 54 have different protein compositions and structures. Therefore, a cross-sectional image reflecting the distribution state of each cell can be acquired based on physical properties and morphology observed with a microprobe microscope.

(Image acquisition method 5. X-ray scattering method)
The fifth example of the method performed in the image acquisition step is to observe the X-ray scattering image of the hair 50 with microbeam X-rays, thereby distributing the plurality of types of fibrous tissues (ortho cells 52a, para cells 52b). This is a method for acquiring a cross-sectional image reflecting the above.
The cuticle cell 51, the two types of cortex cells (ortho cell 52a, para cell 52b), and the medulla cell 54 have different X-ray scattering images because of different microscopic structures. Therefore, by using microbeam X-rays, a cross-sectional image reflecting the distribution state of each cell can be acquired based on the difference in X-ray scattering images.

(Image acquisition method 6. Gene observation method)
A sixth example of the method performed in the image acquisition step is a cross section reflecting the distribution state of a plurality of types of fibrous tissues (ortho cells 52a, para cells 52b) by observing gene expression behavior in human hair follicles. This is a method for acquiring an image.
The cuticle cell 51, the two types of cortex cells (ortho cell 52a, para cell 52b), and medulla cell 54 have different protein compositions, and therefore have different genes (mRNA) expressed in the hair follicle. Therefore, by observing the gene expression behavior in the hair follicle, a cross-sectional image reflecting the distribution state of each cell can be acquired based on the difference in gene expression.

The specific image acquisition process can be performed by selecting one or more of the above.
Among these, from the viewpoint of being hardly affected by hair color, among the above, dyeing method using a fluorescent dye, TEM observation method, spectrum measurement method (infrared absorption spectrum method), microprobe observation method, X-ray Scattering methods and gene observation methods are preferred. From the viewpoint of spatial resolution, a staining method, a TEM observation method, a microprobe observation method, an X-ray scattering method, and a gene observation method are preferable. From the viewpoint of simplicity, a staining method, a spectrum measurement method (infrared absorption spectrum method, Raman spectrum method), and a microprobe observation method are preferable.
As described above, it is possible to obtain image information reflecting the distribution state of a total of four types of cells including the cuticle cell 51 and the two types of cortex cells (ortho cell 52a, para cell 52b) and, in some cases, the medulla cell 54. it can. Hereinafter, unless otherwise noted, the four types of cells refer to cuticle cells 51, ortho cells 52a, para cells 52b, and medulla cells 54.

<Data acquisition process>
In this step, numerical information indicating the distribution state of a plurality of types of fibrous tissues (ortho cells 52a and para cells 52b) is acquired by image analysis of the photographed cross-sectional images.
More specifically, the data acquisition step includes a step of analyzing the photographed cross-sectional image (analysis step) and a step of acquiring numerical information based on the result of the image analysis (numericalization step).

(Analysis process)
In the analysis step, for example, four types of cell distribution existing in human hair are imaged by digital processing of the cross-sectional image or the like.
Here, the cuticle cell 51, the cortex cell 52, and the medulla cell 54 can be easily distinguished from each other on the cross-sectional image of the hair 50 based on the difference in cell shape and the like. For example, since the cuticle cell 51 exists in a layered manner on the surface of the hair 50, the cuticle cell 51 and the cortex cell 52 can be mechanically distinguished based on the form. Moreover, since the medulla cell 54 existing in the center of the hair 50 is porous particularly in the case of Japanese, it can be mechanically distinguished from the cortex cell 52 based on its form.

On the other hand, regarding the two types of cortex cells 52 of the ortho cells 52a and the para cells 52b, the difference in the morphology is different on the submicron order. Therefore, when the spatial resolution of the cross-sectional image is submicron or less, the two can be distinguished from each other based on the difference in form between the ortho cell 52a and the para cell 52b.

Even when the spatial resolution of the cross-sectional image is coarser than submicron, when the acquired image information reflects the cell structure and protein composition, the ortho cell 52a and the para cell 52b are determined based on the information reflecting them. They can be distinguished from each other. For example, the ortho cells 52a and the para cells 52b are colored or contoured as a whole by a staining method, a spectrum measurement method, an X-ray scattering method, or the like, so that they can be distinguished from each other by the difference in color information or the pattern shape. it can.
In order to clearly image the determined distribution state of the four types of cells by digital processing or the like, the data acquisition unit 20 (see FIG. 1) may paint each cell with a plurality of colors by image processing.

(Numericalization process)
The numerical information acquired in the numerical process of the present method is not particularly limited, and various parameters for quantitatively describing the hair characteristics can be selected.

Specifically, in this embodiment, (i) the distance between the centers of gravity of the fibrous tissue, (ii) the abundance ratio of the fibrous tissue, (iii) the cross-sectional second moment of the para cells, (iv) the dispersion of the fibrous tissue Degree is exemplified as numerical information. Hereinafter, (i) to (iv) will be specifically described.

In the present embodiment, the distance between the center of gravity of the fibrous tissue refers to the distance between the center of gravity of one fibrous tissue (ortho cell 52a) and the center of gravity of another fibrous tissue (para cell 52b) in the cross-sectional image.
The abundance ratio of the fibrous tissue refers to the abundance ratio of the fibrous tissue (either the ortho cell 52a or the para cell 52b) to the cortex cell 52.

In the present embodiment, the cross-sectional secondary moment of the para cell 52b refers to a cross-sectional secondary moment in the weak axis direction in the cross-sectional image.
In addition, the degree of dispersion of the fibrous tissue refers to the degree to which one fibrous tissue (ortho cell 52a) is mixed with another fibrous tissue (para cell 52b).

(I) Distance between center of gravity of fibrous tissue The shape and physical properties of hair 50 differ depending on the location of the four types of cells present in human hair. However, of the four types of cells, the cuticle cell 51 is present near the hair surface in any hair, and the medulla cell 54 is present near the hair center when present. Therefore, regarding the cuticle cell 51 and the medulla cell 54, the difference in the existence site by the hair 50 is small. As a result, the influence on the shape and physical properties of the hair 50 due to the difference in the existence site of the cuticle cell 51 and the medulla cell 54 is small.
On the other hand, the distribution of two types of cortex cells (ortho cells 52a and para cells 52b) within the hair is diverse, and the shape and physical properties of the hair vary greatly depending on the location of these cells.
In particular, the inventors have clarified that the distance between the centers of gravity of the ortho cells 52a and the para cells 52b has a positive correlation with the curl curvature, which is an index value indicating the degree of habit of the hair 50. .

Therefore, the distribution centroid of each of the ortho cells 52a and para cells 52b on the cross section is obtained as a numerical value indicating the difference between the existence sites of these two types of cortex cells, in particular, the bias of the cell distribution, and the ortho cells 52a and 52b. It is effective to calculate the distance between the centers of gravity.
The distance between the centers of gravity is close to 0 when the ortho cells 52a and the para cells 52b are uniformly or isotropically distributed on the hair cross section, and is a numerical value when these cells are unevenly distributed unevenly. growing. Therefore, the distance between the centers of gravity of the ortho cells 52a and the para cells 52b can be used as a numerical value indicating the distribution of the ortho cells 52a and the para cells 52b.

Specifically, in the cross-sectional image of the hair 50, the coordinate average of the pixels constituting the ortho cell 52a can be calculated to determine the center of gravity (face center) position of the ortho cell 52a. The same applies to the para cell 52b. Then, the distance between the centroids of the ortho cell 52a and the para cell 52b can be obtained by calculating the distance between the centroid position of the ortho cell 52a and the centroid position of the para cell 52b.

(Ii) Fibrous tissue abundance ratio Since the four types of cells have different protein compositions, structures, and forms, and have different microscopic physical properties, the physical properties of the hair 50 differ depending on the abundance ratio of these cells.
In particular, the inventors have clarified that the abundance ratio of the para cells 52b in the cortex cells 52 has a positive correlation with the bending elastic modulus, which is an index value indicating the bending rigidity of the hair 50. Yes.

The abundance ratio of these cells can be determined by, for example, the following image analysis. That is, first, the area occupied by the four types of cells in the cross-sectional image of the hair 50 is obtained by integrating the number of pixels included in each cell type. And it is good to calculate the ratio of the area which each cell occupies with respect to the total area (cross-sectional area of the hair 50) of four types of cells.
In addition, the ratio of the area occupied by the ortho cells 52a and the para cells 52b to the total area occupied by the two types of cortex cells 52 of the ortho cells 52a and the para cells 52b is obtained, and the existence ratio of these cells may be calculated. .

(Iii) Sectional secondary moment of fibrous tissue In addition, due to the difference in physical properties between the ortho cells 52a and the para cells 52b, the physical properties of the hair 50 differ depending on the sectional secondary moments of these cells in the sectional image. Here, since the para cell 52b has higher tensile rigidity than the ortho cell 52a, generally, the bending rigidity of the hair 50 becomes higher as the cross-sectional second moment of the para cell 52b in the cross-sectional image is larger.
Since bending of the hair 50 occurs in the direction of the weak axis, it is preferable to set a plurality of axes radially from the center of gravity of the hair 50 and calculate the cross-sectional secondary moment in each axial direction. And it is good to make the axial direction which shows the minimum value into a weak-axis direction, and to acquire the cross-sectional secondary moment of the said direction as a cross-sectional secondary moment of the para cell 52b.

Specifically, in the cross-sectional image of the hair 50, the cross-sectional second moment in the arbitrary axial direction may be integrated from the position and area of the pixel included in the para cell 52b.

(Iv) Dispersion degree of fibrous tissue It has been revealed by the inventors that the permeability of the hair care agent to the ortho cells 52a and the para cells 52b is fast in the ortho cells 52a and slow in the para cells 52b. Therefore, in general, in the hair 50 in which the dispersibility of the para cells 52b is low and a large domain of the para cells 52b exists, the penetration of the hair care agent into the domain of the para cells 52b is suppressed. On the other hand, in the hair 50 with high dispersibility of the para cells 52b, the permeability of the hair care agent is good.
Therefore, in the quantification step, the degree of dispersion of the para cells 52b in the hair 50 may be quantified and acquired.

The degree of dispersion of the para cells 52b can be calculated by various methods.
As the first method, a method that pays attention to the area ratio of the small clusters of the para cells 52b can be mentioned. Specifically, the total area of the para-cell 52b clusters (aggregates) exceeding the predetermined threshold area is calculated, and the total area of the para-cells 52b is distributed with the area occupied by the small clusters equal to or smaller than the above-described threshold area. Degree.
As a second method, there is a method in which a cross-sectional image of the hair 50 is divided into segments, and the content of the para cells 52b in each segment is noted. Specifically, the cross-sectional image of the hair 50 is divided into radial and equal-area segments passing through the center of gravity, and the pixels of the para cells 52b included in each segment are counted. The dispersibility of the para cells 52b in the hair 50 can be quantified by averaging the area ratio of the para cells 52b in each segment.

In the numerical process, in addition to this, a numerical value related to the physical shape such as the cross-sectional area and flatness (long diameter or short diameter length) of the hair 50 may be acquired.

In this method, in addition to the cortex cell 52, numerical information indicating the distribution state of at least one of the cuticle cell 51 or the medulla cell 54 contained in the hair can be further acquired.
In particular, in addition to the cortex cell 52, numerical information indicating the distribution state of the cuticle cell 51 may be acquired together.

Here, since the cuticle cell 51, cortex cell 52, and medulla cell 54 constituting the human hair 50 are greatly different from each other, the properties of the hair 50 are different depending on the abundance ratio of these cells.
Since the cuticle cell 51 has a higher cystine content and a higher density of disulfide bonds inside the cells than other cells, it is generally the hardest cell among the above three types of cells. Since the cuticle cell 51 exists at a position far from the center so as to cover the surface of the hair, it particularly contributes greatly to the bending stress and torsional rigidity of the hair fiber.
Therefore, the hair with more cuticle cells 51 exhibits greater bending stress and torsional rigidity. Therefore, by acquiring the distribution state of the cuticle cell 51 as numerical information together with the cortex cell 52, it is possible to evaluate the hair characteristics more accurately.

In this method, using the acquired numerical information, the shape (curvature) of the hair 50 and the mechanical properties of the hair fiber may be evaluated as the characteristics of the hair 50.

Specifically, in addition to the image acquisition process and the data acquisition process, a reference acquisition process and an evaluation process may be further performed.
In the reference acquisition step, calibration data indicating the relationship between numerical information and hair characteristics is acquired using a human hair sample as a reference hair.
In the evaluation step, the hair characteristics relating to the hair are evaluated from the numerical information of the hair acquired in the data acquisition step and the calibration data.

The reference acquisition process may be performed after the image acquisition process or the data acquisition process, or may be performed before these processes.

<Standard acquisition process>
In this step, the above numerical information is acquired using a hair sample whose hair properties such as elastic modulus are known in advance as a reference hair. The reference hair may be one or plural (many).
Then, by acquiring numerical information and hair characteristics related to the reference hair as reference point data or calibration data, the hair characteristics can be estimated using the newly acquired numerical information of the target hair.
Specifically, the hair characteristics of the target hair may be simply compared using one reference hair as a comparison reference. Alternatively, based on a large number of reference hairs, the relationship between specific numerical information and hair characteristics is acquired as a table or function as a calibration curve. And you may estimate the characteristic of the said hair from the numerical information and calibration curve of object hair.

More specifically, for example, for a large number of reference hairs with known curl radii and curl curvature, the cross-sectional image is image-analyzed to calculate the distance between the center of gravity of the ortho cell 52a and the para cell 52b. Then, the correlation function may be obtained by statistically processing the relationship between the curl radius or the curl curvature and the distance between the centers of gravity of the ortho cells 52a and the para cells 52b.

<Evaluation process>
In this step, the numerical information (distance between the centers of gravity of the ortho cells 52a and the para cells 52b) acquired from the cross-sectional image of the target hair 50 is applied to, for example, the correlation function described above, so The curl radius or curl curvature, which is an index value indicating the degree, is calculated.

In this method, instead of the curl radius and the curl curvature, the bending elastic modulus and the existence ratio of the para cells 52b may be obtained for a large number of reference hairs, and the correlation thereof may be calculated statistically.
Then, a bending elastic modulus that is an index value indicating the bending rigidity of the hair may be calculated from the existence ratio of the para cells 52b in the hair 50 to be evaluated.

In the data acquisition apparatus 100 of the present embodiment shown in FIG. 1, the reference acquisition process and the evaluation process can be performed by the data acquisition unit 20 (PC main body 21). In this case, calibration data may be stored in the storage unit of the personal computer main body 21 in advance.

<Output process>
A captured image of the hair 50, a cross-sectional image in which each cell is visualized, acquired numerical information, and part or all of the information indicating the evaluation result may be output from the data acquisition device 100 by any means. A cross-sectional image in which the hair 50 to be evaluated is visualized and a cross-sectional image in which the reference hair is visualized may be displayed side by side.

As the output means, a display 40 provided in the personal computer main body 21, a display connected to the personal computer main body 21 through the communication line 30 or wirelessly, a print output from the printer 41, and the like can be arbitrarily used.

Note that the various components of the data acquisition device 100 of the present embodiment need only be formed so as to realize their functions. For example, the data acquisition unit 20 includes dedicated hardware that exhibits a predetermined function, a data processing device to which the predetermined function is given by a computer program, a predetermined function that is realized in the data processing device by a computer program, and any of these It can be realized as a combination.
In addition, the data acquisition device 100 of the present embodiment can read a computer program and execute a corresponding processing operation, so that a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), an I / O It can be implemented as hardware constructed by a general-purpose device such as an F (Interface) unit, a dedicated logic circuit constructed so as to execute a predetermined processing operation, a combination thereof, or the like.

According to the hair characteristic data acquisition technology of the above embodiment, parameters such as the degree of comb hair that greatly affects the gloss of the hair and the flexural modulus that greatly affects the volume and softness of the hair are quantitatively expressed. It can be acquired as information. For this reason, based on numerical information acquired from the target hair, it is possible to evaluate these hair characteristics, or to provide objective information for assisting selection of an appropriate hair treatment method and hair care agent. it can.

In the conventional case of calculating the curl radius and curl curvature by actually measuring the overall curved shape of the hair, the hair properties such as curl radius, curl curvature, and bending elastic modulus obtained from a single hair are only one value each. It was. On the other hand, in the hair characteristic data acquisition technique of the present embodiment, by acquiring a plurality of cross-sectional images and numerical information from different length positions in one hair, the hair for each length position in one hair. Characteristics can be calculated. Thereby, according to this embodiment, the characteristic of one hair can be evaluated from many aspects.

Furthermore, in the case of the conventional calculation method, a predetermined hair length is required for measurement of hair characteristics. In addition, since the tip of a piece of hair is composed of old cells, the evaluated hair characteristics are such that, in the current state of hair, the influence of the tip of the hair is strongly influenced when measuring the hair characteristics. No, it shows the characteristics of past hair. On the other hand, in the case of this embodiment, since numerical information can be acquired from the cross-sectional image of the root part of hair, the hair characteristic regarding the present hair quality can be evaluated. Then, it is possible to predict future hair characteristics such as the degree of shampoo and bending elastic modulus when the current hair grows.

Hereinafter, the present invention will be described in more detail using examples. Hereinafter, unless otherwise noted, reference numerals of element names correspond to those in FIG.

(Example 1: Comb hair)
This example relates to a method of acquiring numerical information describing the shape and physical properties of hair by acquiring a cross-sectional image of hair using a transmission electron microscope (TEM) and performing image analysis.

[Get image information]
Using a transmission electron microscope (TEM), an image of hair cross-section that has been electronically stained with osmic acid and uranyl acetate was obtained, and image information reflecting the distribution of cells in the hair based on the difference in cell morphology Acquired.

The hair of interest was obtained from the root near the scalp of white woman A in her 30s who did not perform chemical hair treatment such as perm, bleach, or hair color. The target hair was cut into a length of about 12 mm from the root to obtain a hair sample.
The prepared hair sample was washed with shampoo, thoroughly rinsed with ion exchange water, and then dried. When the curl radius of the dried hair sample was measured, the curl radius was 0.9 cm. Such a curl radius is a value classified as a peculiar hair.

The hair sample was dyed by immersing it in a 0.05M phosphate buffer solution (pH 6.7) containing 1.0% by mass of osmic acid for 1 hour, and then rinsed with excess ionized water with ion-exchanged water and dried. I let you.
Next, after embedding a hair sample dyed with osmic acid in an epoxy resin, a hair cross section having a thickness of 100 nm was cut out using a microtome and fixed on a copper mesh for a transmission electron microscope (TEM).
The hair cross section fixed on the copper mesh was dipped in a 2.0% by mass uranyl acetate aqueous solution for 4 hours, followed by rinsing excess uranyl acetate with ion-exchanged water and drying it. The hair cross-section image was obtained by observing the hair cross-section double-stained with osmic acid and uranyl acetate with a transmission electron microscope (TEM).

[Visualization of cell distribution]
The whole image of the hair cross section obtained when the double dyed hair cross section is observed with a transmission electron microscope (TEM) is shown in FIG.
4 to 7 show typical images of the cuticle cell 51, para cell 52b, ortho cell 52a, and medulla cell 54, respectively, taken at high magnification. 3 to 7, scales are shown in the drawings. FIG. 3 is an image obtained by collecting and reconstructing high-magnification images as shown in FIGS.

In FIGS. 3 to 7, the black granular portion having a diameter of about 0.2 μm is a melanin granule. In addition, the amorphous black portion is a nucleus residue of the cortex cell 52. And the part which has fallen white is the cavity inside hair, or the embedding resin outside hair.

Cuticle cells 51, para cells 52b, ortho cells 52a and medulla cells 54 were identified using FIGS. 4 to 7 which are high-magnification images, and the results were applied to FIG.

As shown in FIG. 4, since the cuticle cell 51 exists in a layered manner near the surface of the hair, two adjacent cortex cells 52 (para cell 52b and ortho cell 52a) are mutually connected based on the difference in form. Can be determined.
Further, as shown in FIG. 7, the medulla cell 54 exists in the center of the hair and has a porous structure, and therefore can be distinguished from the two types of cortex cells 52 based on the difference in form. .

The two types of cortex cells 52, the para cell 52b and the ortho cell 52a, are different in submicron order, and therefore can be mechanically distinguished from each other based on the form.
As shown in FIG. 5, in the para cell 52b, macrofibrils are fused to form a relatively large micron-order domain.
On the other hand, as shown in FIG. 6, the ortho cell 52 a shows a form in which macrofibrils having a size of submicron order are gathered.
The para cell 52b and the ortho cell 52a can be discriminated based on the difference in the form of the macrofibril.

The cross-sectional image of FIG. 3 is image-analyzed according to the discriminant criteria based on the above differences, and the four types of cells inside the hair (cuticle cell 51, ortho cell 52a, para cell 52b, medulla cell 54) are sequentially black. FIG. 8 shows a visualized image obtained by painting with dark gray, light gray, and white. In FIG. 8, regions other than hair are shown in a lattice pattern. Moreover, the scale of FIG. 8 is the same scale as FIG.

From FIG. 8, in addition to the black cuticle cells 51 and the white medulla cells 54, the distribution state of the light gray para cells 52b and the dark gray ortho cells 52a on the hair cross section is clearly visualized. Recognize.
The light gray para cells 52b are slightly distributed in the upper left on the hair cross section in FIG. 8, whereas the dark gray ortho cells 52a are distributed in the lower right on the hair cross section. It is visualized.
That is, in the hair used in this example, it was found that the distribution of the para cells 52b and the ortho cells 52a in the cross-sectional image was biased as shown in FIG.

[Numericalization of cell abundance]
Based on the visualized image of FIG. 8, the area occupied by the four types of cells on the hair cross section and the area of the hair cross section (total cross sectional area) were determined by image analysis. The results are shown below.

Area occupied by the cuticle cell: 491 μm ²
Area occupied by ortho cells: 984 μm ²
Area occupied by para cells: 1681 μm ²
Area occupied by medulla cells: 47 μm ²
Area of hair cross section: 3202 μm ²

Furthermore, the area ratio occupied by each cell was determined. The results are shown below.

Cuticle cell area ratio: 15.3%
Area ratio of ortho cells: 30.7%
Area ratio of para cells: 52.5%
Medura cell area ratio: 1.5%

Next, the area ratio of the para cells 52b and the ortho cells 52a to the total area occupied by the two types of cortex cells (ortho cells 52a and para cells 52b) was determined. The results are shown below.

Ortho cell area ratio: 36.9%
Para cell area ratio: 63.1%

[Numericalization of the location of each cell]
Based on the visualized image of FIG. 8, the gravity center positions of the para cells 52b and the ortho cells 52a on the hair cross section of the present embodiment are obtained by image analysis, and the distance between the gravity centers of the para cells 52b and the ortho cells 52a is obtained. It was 5 μm.

(Example 2: Straight hair)
In the present embodiment, the target hair is changed, image analysis is performed in the same manner as in the first embodiment, and the distance between the centers of gravity of the para cells 52b and the ortho cells 52a is obtained.
[Get image information]
The hair which is the object of this example is a hair sample of a white woman in the thirties who has not been subjected to chemical hair treatment such as perm, bleach, or hair color, collected from the base near the scalp.
When the curl radius was measured after washing and drying in the same manner as in Example 1, the curl radius was 5.0 cm.
From this hair sample, a hair section double-dyed with osmic acid and uranyl acetate was obtained in the same manner as in Example 1.
FIG. 9 shows a hair cross-sectional image obtained when the double-stained hair cross-section is observed with a transmission electron microscope.

[Visualization of cell distribution]
In the same manner as in Example 1, the cross-sectional image of FIG. 9 was image-analyzed, and the four types of cells (cuticle cell 51, ortho cell 52a, para cell 52b, medulla cell 54) inside the hair were sequentially black and dark gray, respectively. FIG. 10 shows a visualized image obtained by painting with light gray and white.
In FIG. 10, regions other than hair are shown in a lattice pattern. 10 is the same scale as FIG.

In the case of this hair, the white medulla cells 54 do not exist, but from FIG. 10, in addition to the black cuticle cells 51, the distribution state of the light gray para cells 52b and the dark gray ortho cells 52a on the hair cross section is shown. It was found that it was clearly visualized. The light gray para cells 52b are distributed in the central part on the hair cross section of FIG. 10, while the dark gray ortho cells 52a are visualized in the peripheral part.

[Numericalization of cell abundance]
Based on the visualized image of FIG. 10, the area occupied by the four types of cells and the area of the hair cross section on the hair cross section were determined by image analysis. The results are shown below.

Area occupied by the cuticle cell: 633 μm ²
Area occupied by ortho cells: 1184 μm ²
Area occupied by para cells: 1376 μm ²
Area occupied by medulla cells: 0 μm ²
Hair cross-sectional area: 3193 μm ²

Furthermore, the area ratio occupied by each cell was determined. The results are shown below.

Cuticle cell area ratio: 19.8%
Area ratio occupied by ortho cells: 37.1%
Area ratio of para cells: 43.1%
Medura cell area ratio: 0.0%

Next, the area ratio of the para cells 52b and the ortho cells 52a to the total area occupied by the two types of cortex cells (ortho cells 52a and para cells 52b) was determined. The results are shown below.

Ortho cell area ratio: 46.2%
Para cell area ratio: 53.8%

[Numericalization of the location of each cell]
Based on the visualized image in FIG. 10, the gravity center positions of the para cells 52b and the ortho cells 52a on the hair cross section of this example were obtained by image analysis, and the distance between the gravity centers of the para cells 52b and the ortho cells 52a was obtained. .1 μm.

The hair of Example 2 is almost straight hair with a curl radius of 5.0 cm, but the distribution of para cells 52b and ortho cells 52a on the hair cross section was isotropic as shown in FIG. The distance between the centers of gravity of these two types of cortex cells 52 was 2.1 μm, which was a small value compared to Example 1 (curl radius 0.9 cm, distance between centers of gravity 8.5 μm).

Further, comparison between Example 1 and Example 2 revealed that the curl radius of hair and the distance between the centers of gravity of the two types of cortex cells 52 had a positive correlation.

(Example 3: Straight hair)
In this embodiment, a hair cross-section image reflecting the cell distribution state is obtained by dyeing the hair cross-section with two types of dyes, and numerical information describing the shape and physical properties of the hair is obtained by image analysis. Regarding the method.

[Get image information]
A cross section of the hair was stained with yellow 202 with yellow-green fluorescence and sulfododamine 101 with orange fluorescence. As a result, of the two types of cortex cells 52, the para cells 52b were stained yellow-green, and the ortho cells 52a were stained orange. And the image information reflecting the distribution state of the cell in the dyed hair inside was acquired.

The target hair is the hair of Japanese woman C in her 30s who has not performed chemical hair treatment such as perm, bleach, hair color, etc., collected from the base near the scalp.
When the curl radius was measured after washing and drying in the same manner as in Example 1, the curl radius was 3.9 cm.
After embedding this hair sample in an epoxy resin, a hair cross section having a thickness of 1.5 μm was cut out using a microtome and fixed on a slide glass.

The hair cross-section fixed on the slide glass was sequentially stained with yellow No. 202 (Acid Yellow 73) and sulferodamine 101 according to the method described in Non-Patent Document 4 described above. Specifically, the hair cross-section was immersed in a 0.002% by weight yellow 202 (Acid Yellow 73) aqueous solution for 18 hours, rinsed with ion-exchanged water, and then dried. Subsequently, it was immersed in 0.0005% by mass of sulfododamine 101 aqueous solution, rinsed with ion-exchanged water, and then dried to obtain a hair cross section dyed with two kinds of dyes.

FIG. 11 shows a hair cross-sectional image obtained when a hair cross-section dyed with two types of fluorescent dyes is observed with a fluorescence microscope. FIG. 11 is a black and white binarized image of a hair cross-sectional image acquired as a color image.
12 to 14 show the RGB values of the acquired color image, respectively. Specifically, FIG. 12 shows an R value image, FIG. 13 shows a G value image, and FIG. 14 shows a B value image.
In the case of this example, the structure of the hair cross section shown in FIG. 11 can be clearly identified by the G value image (FIG. 13). Specifically, a portion stained with a yellow-green fluorescent dye (yellow 202) is shown in a relatively light color in FIG. In addition, the site | part dye | stained with the orange fluorescent dye (sulfordamine 101) is shown by the comparatively light color in FIG. That is, it can be seen from FIGS. 11 to 14, particularly FIGS. 12 and 13, that the hair cross-section is dyed into two colors of yellow-green and orange.

[Visualization of cell distribution]
In the case of the present example, a site stained with a yellow-green fluorescent dye (yellow 202) is defined as a para cell 52b.
In addition, the sites stained with the orange fluorescent dye (sulforudamine 101) are ortho cells 52a, cuticle cells 51, and medulla cells 54. These three types of cells that are stained with an orange fluorescent dye can be distinguished from each other because they have different locations and forms. For example, since the cuticle cell 51 is present in a layered manner in the vicinity of the hair surface, it can be distinguished from the adjacent ortho cell 52a based on the difference in form. Further, since the medulla cell 54 exists in the center of the hair and has a porous structure, it can be distinguished from the ortho cell 52a based on the difference in form.
The image information of FIG. 11 was image-analyzed according to the above discrimination criteria based on the difference in stained color and the difference in form. FIG. 15 shows visualized images obtained by painting four types of cells (cuticle cell 51, ortho cell 52a, para cell 52b, and medulla cell 54) inside the hair in black, dark gray, light gray, and white, respectively. In FIG. 15, regions other than hair are shown in a lattice pattern.
Further, the visualized image shown in FIG. 15 may be created based on FIG. In this case, a site where the G value is greater than or equal to a certain threshold value is determined to be yellow-green (para cell 52b), and a region equal to or less than this threshold value is determined to be orange (ortho cell 52a, cuticle cell 51 or medulla cell 54). The orange portion can be identified as the ortho cell 52a, the cuticle cell 51, and the medulla cell 54 based on the difference in form.

[Numericalization of cell abundance]
Based on the visualized image in FIG. 15, the area occupied by the four types of cells on the hair cross section and the area of the hair cross section were determined by image analysis. The results are shown below.

Area occupied by the cuticle cell: 1132 μm ²
Area occupied by ortho cells: 4412 μm ²
Area occupied by para cells: 3022 μm ²
Area occupied by medulla cells: 175 μm ²
Hair cross-sectional area: 8741 μm ²

Furthermore, the area ratio occupied by each cell was determined. The results are shown below.

Cuticle cell area ratio: 12.9%
Area ratio of ortho cells: 50.5%
Area ratio occupied by para cells: 34.6%
Medura cell area ratio: 2.0%

Next, the area ratio of the para cells 52b and the ortho cells 52a to the total area occupied by the two types of cortex cells (ortho cells 52a and para cells 52b) was determined. The results are shown below.

Ortho cell area ratio: 59.3%
Para cell area ratio: 40.7%

[Numericalization of the location of each cell]
Based on the visualized image of FIG. 15, the gravity center positions of the para cells 52b and the ortho cells 52a on the hair cross section of this example were obtained by image analysis, and the distance between the gravity centers of the para cells 52b and the ortho cells 52a was obtained. 0.7 μm.

The hair of Example 3 has a curl radius of 3.9 cm and is slightly straight hair, but the distribution of the para cells 52b and the ortho cells 52a on the hair cross section is slightly biased as shown in FIG. The distance between the centers of gravity of the two types of cortex cells 52 was 4.7 μm.
When the results of Examples 1, 2, and 3 are compared, the order of the curl radii matches the order of the distances between the centers of gravity of the two types of cortex cells 52. Therefore, it was further clarified that there is a positive correlation between the curl radius of the hair and the distance between the centers of gravity of the two types of cortex cells 52.

(Example 4: Comb hair)
In this example, the target hair is changed, and image analysis is performed in the same manner as in Example 3, and the distance between the centers of gravity of the para cells 52b and the ortho cells 52a is obtained.
[Get image information]
The hair which is the object of this example was obtained from the roots near the scalp of Japanese female D in his 20s who did not perform chemical hair treatment such as perm, bleach or hair color.
After washing and drying in the same manner as in Example 3, the curl radius of the hair sample was measured and found to be 0.55 cm.
As in Example 3, after embedding this hair sample in an epoxy resin, a hair cross-section having a thickness of 1.5 μm was cut out using a microtome, and yellow No. 202 (Acid Yellow 73) and sulfododamine 101 were used. A dyed hair cross section was obtained.

FIG. 16 shows a hair cross-sectional image obtained when a hair cross section dyed with two types of fluorescent dyes is observed with a fluorescence microscope. FIG. 16 is a black and white binarized image of a hair cross-sectional image acquired as a color image.
17 to 19 are images obtained by converting the RGB values of the acquired color image, respectively. Specifically, FIG. 17 shows an R value image, FIG. 18 shows a G value image, and FIG. 19 shows a B value image.
Also in this example, like the example 3, the structure of the hair cross section shown in FIG. 16 can be identified particularly clearly by the G value image (FIG. 18).

[Visualization of cell distribution]
Similarly to Example 3, the image information of FIG. 16 is image-analyzed, and four types of cells (cuticle cell 51, ortho cell 52a, para cell 52b, medulla cell 54) inside the hair are respectively black, dark gray, and light. A visualized image obtained by painting in gray and white is shown in FIG.
In FIG. 20, regions other than hair are shown in a lattice pattern.

[Numericalization of cell abundance]
Based on the visualized image of FIG. 20, the area occupied by the four types of cells and the area of the hair cross section on the hair cross section were determined by image analysis. The results are shown below.

Area occupied by the cuticle cell: 847 μm ²
Area occupied by ortho cells: 3181 μm ²
Area occupied by para cells: 1959 μm ²
Area occupied by medulla cells: 159 μm ²
Area of hair cross section: 6145 μm ²

Furthermore, the area ratio occupied by each cell was determined. The results are shown below.

Cuticle cell area ratio: 13.8%
Area ratio of ortho cells: 51.8%
Area ratio of para cells: 31.9%
Medura cell area ratio: 2.6%

Next, the area ratio of the para cells 52b and the ortho cells 52a to the total area occupied by the two types of cortex cells (ortho cells 52a and para cells 52b) was determined. The results are shown below.

Ortho cell area ratio: 61.9%
Para cell area ratio: 38.1%

[Numericalization of the location of each cell]
Based on the visualized image of FIG. 20, the gravity center positions of the para cells 52b and the ortho cells 52a on the hair cross section of the present example are obtained by image analysis, and the distance between the gravity centers of the para cells 52b and the ortho cells 52a is obtained. .4 μm.

The hair of Example 4 had a curl radius of 0.55 cm and was a very strong peculiar hair, but the distribution of the para cells 52b and the ortho cells 52a on the hair cross section was considerably biased as shown in FIG. The distance between the centers of gravity of these two types of cortex cells 52 was 20.4 μm.
When the results of Examples 1, 2, 3, and 4 are compared, the order of the curl radii matches the order of the distances between the centers of gravity of the two types of cortex cells 52. Therefore, it was further clarified that there is a positive correlation between the curl radius of the hair and the distance between the centers of gravity of the two types of cortex cells 52.

(Example 5: Comparison with reference hair 1)
For 41 reference hairs with different curl radii, as in the case of the TEM observation method of Example 1, an image information acquisition step, a cell distribution visualization step, and a quantification step for each cell were performed.
FIG. 21A is a scatter diagram showing the relationship between the curl radius of the reference hair thus obtained and the distance between the centers of gravity of the para cells 52b and the ortho cells 52a.

When the relational expression between the curl radius and the distance between the center of gravity was obtained by the least square method based on the figure, the relation of the following expression (1) was obtained. In the figure, a graph of the following formula (1) is displayed as calibration curve data of the curl radius and the distance between the center of gravity.

Curl radius / cm = -0.22 x center of gravity distance / μm + 6.3 Equation (1)

Based on this relational expression (1), the curl radius of the hair of a Japanese woman A in her twenties (distance between centers of gravity = 4.7 μm) was estimated to be 5.4 cm. This predicted curl radius was in good agreement with the measured curl radius (6 cm).

(Example 6: Comparison with reference hair 2)
For the 41 reference hairs used in Example 5, the abundance ratio of para cells 52b in the cortex cells 52 and the flexural modulus were determined.
FIG. 21 (b) is a scatter diagram showing the relationship between the flexural modulus obtained in this way and the abundance ratio of the para cells 52b.

When the relational expression between the flexural modulus and the abundance ratio of the para cells 52b was obtained by the least square method based on the same figure, the relation of the following expression (2) was obtained. In the figure, a graph of the following formula (2) is displayed as calibration curve data of the flexural modulus and the existence ratio of para cells.

Bending elastic modulus / GPa = 0.45 × para cell existing ratio + 0.67 Equation (2)

By using the above formulas (1) and (2), it is only necessary to obtain the distance between the center of gravity of the ortho cells 52a and the para cells 52b in the target hair and the existence ratio of the para cells 52b to the cortex cells 52. The curl radius and bending elastic modulus of hair can be evaluated.
According to the data acquisition method and data acquisition device 100 of this embodiment, quantitative indicators for describing various characteristics of a hair sample can be acquired from cross-sectional images of hair.

This application claims priority based on Japanese Patent Application No. 2009-181066 filed on Aug. 3, 2009, the entire disclosure of which is incorporated herein.

Claims (10)

An image acquisition step of acquiring a cross-sectional image of the hair, wherein a plurality of types of fibrous tissues constituting cortex cells contained in human hair are visualized so as to be distinguishable from each other;
A data acquisition step of acquiring numerical information indicating the distribution states of the plurality of types of fibrous tissues visualized from the cross-sectional image. Using a human hair sample as a reference hair, a reference acquisition step for acquiring calibration data indicating the relationship between the numerical information and the hair characteristics;
An evaluation step for evaluating the hair characteristics of the hair from the numerical information of the hair acquired in the data acquisition step, and calibration data indicating the relationship between the numerical information and the hair characteristics using a human hair sample as a reference hair; The method for acquiring hair characteristic data according to claim 1, further comprising: The acquisition of hair characteristic data according to claim 2, wherein in the data acquisition step, a distance between the center of gravity of one fibrous tissue and the center of gravity of another fibrous tissue in the cross-sectional image is acquired as the numerical information. Method. 4. The method for acquiring hair characteristic data according to claim 3, wherein an index value indicating a degree of hair habit is calculated in the evaluation step. 3. The method for acquiring hair characteristic data according to claim 2, wherein, in the data acquisition step, the ratio of the fibrous tissue to the cortex cells is acquired as the numerical information. The method for acquiring hair characteristic data according to claim 5, wherein an index value indicating the bending stiffness of the hair is calculated in the evaluation step. The hair characteristic data according to any one of claims 1 to 6, wherein in the data acquisition step, numerical information indicating a distribution state of at least one of cuticle cells or medulla cells contained in the hair is further acquired. How to get The method for acquiring hair characteristic data according to any one of claims 1 to 7, further comprising a step of dyeing a cross section of the hair with a dye and visualizing a plurality of types of the fibrous tissues in a distinguishable manner. By measuring the infrared absorption spectrum or Raman spectrum of the hair cross section, or scanning the hair cross section with a microprobe microscope, or observing the hair with a transmission electron microscope, a plurality of types of the fibrous The method for acquiring hair characteristic data according to any one of claims 1 to 7, further comprising a step of visualizing the tissues so as to be distinguishable from each other in the cross-sectional image. An image acquisition means for acquiring a cross-sectional image of the hair, wherein a plurality of types of fibrous tissues constituting cortex cells contained in human hair are visualized so as to be distinguishable from each other;
A data acquisition unit for acquiring numerical information indicating the distribution state of the plurality of types of visualized fibrous tissues from the cross-sectional image.

Images